## Using rownames(importance_df) as id variables

Can we predict the endpoint C. difficile CFU with the community structure at Day 0?

Endpoint has data from Day 2,3,10 - might be misleading since contains CFU from different data points

Does Day Community structure predict C. difficile CFU of that day?

RF all mice/days together RF cfu vs community by day

## Using rownames(importance_df_day) as id variables

Does removing the early euthanized samples improve prediction in days 1 to 3?

## Using rownames(importance_persist) as id variables

## Using rownames(importance_persist_day) as id variables

Eliminating mice euthanized early from the RF model gives similar R^2 and MSE, bu there is a slight advantage to community OTU features of Day 0 to predict Day1 CFU. As well as the R^2 value increases with increasing features, whereas when all mice are used the day 1 R^2 only decreases with increasing features. Of note, accompaied by this is an increase in the % MSE attributed to OTU15 (Akkermansia), which has seemed to stand out in all other days/analysis.Interestingly, akkermansia does not appear to have the same relationship when compared to the same day cfu and community. This could suggest akkermansia is promoting the intial colonization of c difficile. At first glance, OTU135 (Coriobacteriaceae) seems to stand out for predicting cfu of the same day as well as from day 0.

Can we predict moribundity with the community structure at Day 0?

## 
## Call:
## roc.default(response = as.numeric(Predict_early_euth_df$Euth_Early),     predictor = as.numeric(rf_early_euth$predicted))
## 
## Data: as.numeric(rf_early_euth$predicted) in 39 controls (as.numeric(Predict_early_euth_df$Euth_Early) 1) < 16 cases (as.numeric(Predict_early_euth_df$Euth_Early) 2).
## Area under the curve: 0.7812

Confusion Matrix

No Yes class.error
No 39 0 0.0000
Yes 7 9 0.4375

OOB error rate = 12.7272727

Mice which were euthanized early, but the model predicts the will were not (Mouse Tag - Cage): 2084 IN1, 2091 OP, 2096 OUT, 2542 OP, 382 578, 389 578, NT INA